Classification of impacts from sensor data

ABSTRACT

Systems and methods are provided for classifying an impact to a head of a human being as one of a plurality of event classes. A sensor interface is configured to determine an acceleration at a location of interest on the human being from provided sensor data in response to the impact. A feature calculation component is configured to calculate a plurality of impact parameters from the determined acceleration at the location of interest. A pattern recognition classifier is configured to associate the impact with an associated event class of at least three associated event classes according to the calculated plurality of impact parameters. A post-processing component is configured to communicate the associated event class an observer via an associated output device.

RELATED APPLICATIONS

This application claims priority from each of U.S. ProvisionalApplication No. 61/364,639, filed 15 Jul. 2011, and U.S. ProvisionalApplication No. 61/444,281, filed 18 Feb. 2011. The subject matter ofboth applications is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to systems and methodologies forevaluating mechanical forces, and, in particular, is directed to systemsand methods for classifying impacts from sensor data.

BACKGROUND OF THE INVENTION

There are over forty-seven million athletes under the age of twenty-fourwho participate in contact sports like football, basketball, hockey,soccer, boxing, and mixed martial arts (MMA) each year in the UnitedStates. Each of these young athletes is at risk for concussive traumaticbrain injuries (cTBI) and long-term brain dysfunction due to repeatedhead impact. These young athletes, with developing neurological systems,sustain a large portion of the 3.8 million cTBI occurring yearly and areat heightened risk of developing deleterious long-term neurological,physiological and cognitive deficits. The head impact conditionsresponsible for cTBI and potential long-term deficits in athletes areunknown.

SUMMARY OF THE INVENTION

In accordance with an aspect of the present invention, a system isprovided for classifying an impact to a head of a human being as one ofa plurality of event classes. A sensor interface is configured todetermine an acceleration at a location of interest on the human beingfrom provided sensor data in response to the impact. A featurecalculation component is configured to calculate a plurality of impactparameters from the determined acceleration at the location of interest.A pattern recognition classifier is configured to associate the impactwith an associated event class of at least three associated eventclasses according to the calculated plurality of impact parameters. Apost-processing component is configured to communicate the associatedevent class an observer via an associated output device.

In accordance with another aspect of the present invention, a method isprovided for determining a risk of injury to a human being. At least oneof a linear acceleration, an angular acceleration, an angular velocityand an orientation is measured at a first location on the human being.An acceleration is determined at a location of interest on one of a headand a neck of the human being from the measured at least one of a linearacceleration, an angular acceleration, an angular velocity and anorientation at the first location. The first location is remote from thelocation of interest. A plurality of impact parameters are calculatedfrom the determined acceleration at the location of interest. Thecalculated plurality of impact parameters are associated with an injuryclass of a plurality of injury classes. Each injury class representing arange of probabilities that the human being will suffer an injury to astructure within one of the head and the neck given the calculatedplurality of impact parameters. The associated event class iscommunicated to an observer via an associated output device.

In accordance with another aspect of the present invention, a method isprovided for classifying an impact in a sporting event. At least one ofa linear acceleration, an angular acceleration, an angular velocity andan orientation is measured at a first location on an athleteparticipating in the sporting event. An acceleration is determined at alocation of interest on one of a head and a neck of the athlete duringthe impact from the measured at least one of a linear acceleration, anangular acceleration, an angular velocity and an orientation at thefirst location. The first location is remote from the location ofinterest. A plurality of impact parameters are calculated from thedetermined acceleration at the location of interest. The calculatedplurality of impact parameters are associated with an impact class of aplurality of impact classes. Each impact class represents an associatedtype of impact within the sporting event. The associated impact class iscommunicated to an observer via an associated output device.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features of the present invention will becomeapparent to those skilled in the art to which the present inventionrelates upon reading the following description with reference to theaccompanying drawings, in which:

FIG. 1 illustrates an impact monitoring system configured to detect andcharacterize impacts to a mammalian head;

FIG. 2 illustrates a classification system for classifying an impactinto an associated event class in accordance with an aspect of thepresent invention;

FIG. 3 illustrates one example of a method for determining kinematicsand kinetics at a center of gravity of a user's head in accordance withan aspect of the present invention;

FIG. 4 illustrates one example of a method for classifying an impactinto an event class in accordance with an aspect of the presentinvention;

FIG. 5 illustrates a methodology for utilizing an impact monitoringsystem in accordance with an aspect of the present invention to augmentthe experience of a non-participant of an athletic event;

FIG. 6 illustrates a methodology for utilizing an impact monitoringsystem in accordance with an aspect of the present invention to monitorthe performance and condition of an athlete in an athletic event;

FIG. 7 illustrates a methodology for utilizing additional sensors, thatis, sensors not directly used to measure impacts, placed on a sensorassembly in accordance with an aspect of the present invention tomonitor a status of an athlete during an athletic event;

FIG. 8 illustrates a methodology for enhancing a presentation of anathletic event; and

FIG. 9 illustrates a computer system that can be employed to implementsystems and methods described herein, such as systems and methods basedon computer executable instructions running on the computer system.

DETAILED DESCRIPTION OF THE INVENTION

In accordance with an aspect of the present invention, one or moresensor assemblies for detecting and characterizing impacts to amammalian head are provided. To this end, sensor assemblies can befixedly attached to the head in one or more locations, and themeasurements taken at each location can be used to calculate kinematicsand kinetics taking place at any desired location, including a center ofgravity, within the head or neck. From this data, it is possible toaccurately estimate the effects of a given impact on a user, allowing anobserver to act to protect a user in response to an impact, or sequenceof impacts, ranging in severity from minor to severe.

FIG. 1 illustrates an impact monitoring system 10 configured to detectand characterize impacts to a mammalian head. The term “head” is usedgenerally herein to indicate any portion of the cranium, brain, maxilla,mandible and mouth structures (e.g., teeth), occipito-cervical region,low cervical region (C5, C6, and C7 vertebrae), and associated portionsof the spinal cord, as well as any other head and neck structures whichcould be adversely effected by a directly applied and/or a transmittedimpact force (e.g., the head snaps back after a body tackle). The system10 includes at least one sensor assembly 12 positioned in the vicinityof the head. In accordance with an aspect of the present invention, eachsensor assembly 12 is configured to measure at least one of the linearacceleration, angular velocity, angular acceleration, and orientation ofthe sensor assembly 12 along any desired axis or set of orthogonal axes.In the one implementation, the sensor assembly 12 can includemicroelectromechanical systems (MEMS) sensors configured to measure oneor more of the linear acceleration, angular velocity, angularacceleration and orientation (i.e., angular position) of the head at thepoint of affixation of the sensor. It will be appreciated that thesensor assembly 12 can be placed in any appropriate apparatus that canbe fixedly mounted to, within, or in an otherwise operative relationshipto a mammalian head, such as a helmet, mouthpiece, instrument insertedin the auditory canal, mounted in the nasal canal, affixed to the skin,placed in a headband, inserted into eyewear, or any other suitableapparatus.

In one implementation, the sensor assembly 12 can be mounted within amouthpiece apparatus. Here, a mouthpiece may be a dental/orthodonticappliance (e.g., retainer), mouth guard, lip guard, dental implant(e.g., prosthetic tooth), or any other suitable device located,temporarily or permanently, within a user's oral cavity. In accordancewith an aspect of the present invention, it has been determined that theuse of a mouthpiece provides several advantages. To begin with,participants in many helmeted (e.g., football, military, hockey,lacrosse, amateur boxing, wrestling, motocross, bicycling) andnon-helmeted (basketball, professional boxing and mixed martial arts,soccer, field hockey, rugby, Australian Football, multisport, golf)activities already utilize a protective or vanity mouthpiece, which mayfacilitate quick acceptance and general usage of the described sensingmouthpiece assembly. Further, a firm coupling between the sensorsassociated with the mouthpiece apparatus and the head is achieved fromtight fitting between the teeth and mouthpiece substrate material andsurface tension provided by saliva, and is bolstered by jaw clenchingduring activity or in response to impending impact. A mouthpieceimplementation is thus expected to provide a high degree of kinematicand kinetic calculation accuracy while using a device that is of aformat already familiar to, and accepted by, participants in therecreation, military, or other mouthpiece-using activity.

The mouthpiece can be formed from any appropriate material for absorbingshock between the maxilla (upper jaw) and mandible (lower jaw) and/ormaterial designed to induce cervical spine muscle activation in responseto impending head impact while having sufficient flexibility to conformto the mouth and contain the sensor assembly 12. The sensor assembly 12may be encapsulated entirely by surrounding material of the mouthpiece,embedded partially in the material (e.g., a substrate), and/or placed innon-surrounding contact with the material (e.g., attached to a surfaceof a mouthpiece). In one implementation, the mouthpiece is formed frommultiple layers of material, with one layer including sensors mounted,for example, in an elongate pattern. This allows for quickinsertion/removal of the sensor assembly 12 and allows worn-out parts tobe replaced. The sensor strip can comprise a plurality of MEMS-basedlinear and rotational kinematic sensors.

In an exemplary implementation, the sensor strip includes six embeddedlinear accelerometers, three embedded angular velocity sensors and threeembedded tilt sensors. It will be appreciated, however, that this sensorconfiguration is merely given for the purpose of example, andimplementations using just an array of linear accelerometers or just anarray of angular velocity/angular acceleration sensors are alsoenvisioned. Essentially, any appropriate number and combination oflinear accelerometers, angular accelerometers, angular velocity sensors,or orientation sensors can be utilized in accordance with an aspect ofthe present invention. Example linear accelerometers that can be usedare capable of measuring linear acceleration up to two thousand timesthe standard gravitational acceleration, example angular velocitysensors that can be used are configured to measure angular velocity upto one hundred radians per second, example angular accelerometers thatcan be used are capable of measuring angular acceleration up to fiftythousand radians per second squared and example orientation sensors thatcan be used are configured to measured position in space over a fullthree hundred sixty degree arc, far exceeding typical athlete headimpacts of one or two hundred times the standard gravitationalacceleration, twenty-five to fifty radians per second, five to tenthousand radians per second squared and orientation of one hundredeighty degrees. Each sensor footprint may occupy a volume ofapproximately four millimeters by four millimeters by two millimeters.Further, it will be appreciated that, in accordance with an aspect ofthe present invention because of the generic nature of the algorithmused to calculate localized head kinematics and kinetics, the placementand number of the sensors can be essentially arbitrary, such that nospecific relationship among the positions or type of the plurality ofsensors is required.

The mouthpiece can further include a component for wireless datatransmission to allow the sensor data to be provided to an externalprocessor. For example, the mouthpiece can include a radio frequency(RF) or microwave transmitter operating with an appropriate transmissionprotocol and a miniature antenna.

To facilitate capture and transfer of the data, the mouthpiece caninclude a flash memory accessible in a wired or wireless manner. Forexample, a port can be provided on the mouthpiece to allow data to betransferred to a computer via a universal serial bus (USB) or otherconnection. The sensors and the transmitter can be powered by anon-board battery, which can be shaped to fit the contour of themouthpiece. For example, the mouthpiece can include an on-board wiredtransmitter with data storage. It will be appreciated that themouthpiece can further include physiochemical sensors to monitorinternal body metrics such as, but not limited to, temperature,hydration, pH, glucose level, sodium concentration, oxygen saturation,troponin, and respiration.

In accordance with an aspect of the present invention, the datacollected by the sensors can be provided to a data transform component14 configured to calculate the kinematics and kinetics at a location ofinterest within the head and neck of an individual wearing the sensorassembly 12. The location of interest can include, any location withinthe wearer's head or neck including, for example, any of the frontallobe, parietal lobe, temporal lobe, occipital lobe, cerebellum, medullaoblongata, pons, thalamus, gyrus, formix, amygdyla, hippocampus,cranium, facial bones, maxilla, mandible, cerebrospinal fluid,occipito-cervical junction, cervical spine, vertebral bodies, spinalcord, spinal nerves, spinal vessels, basal ganglia, pen-vascular spaces,septum, white-gray matter junctions, bridging veins, corpus callosum,fissure, cavity sinus, meninges, falx, dura, arachnoid, and pia matterof the wearer. The location of interest can also include a center ofgravity of the wearer's head or head/neck mass.

It will be appreciated that the data transform component 14 can beimplemented as dedicated hardware, software executed on a generalpurpose processor, or some combination of dedicated hardware andsoftware. Further, the data transform component 14 can be implemented ona platform associated with the sensors (e.g., a mouthpiece or helmet),in a processing unit worn by the player either hardwired or wirelesslyconnected to the sensor assembly, at a remote location, or distributedacross multiple discrete processing components. One or more of linearacceleration, angular acceleration, angular velocity, and orientationare measured at the sensor assembly, and data derived from headanthropometry can be used to calculate corresponding linear and angularhead kinetics and kinematics at any location on or within the head andneck. For example, the position of each sensor assembly 12 relative tothe location of interest can be determined and registered at the datatransform component 14.

In one implementation, the location of interest can be represented as astatic location during the impact, and translation of the sensor data atthe data transform component 14 can be accomplished according to thefollowing “rigid body” relationship between the measured kinematics at asensor, ã_(mouth)(t), {tilde over (ω)}(t), {tilde over (α)}(t) and theacceleration at the location of interest, ã_(LOC)(t):

{tilde over (α)}_(LOC)(t)={tilde over (α)}_(mouth)(t)+{tilde over(ω)}(t)×({tilde over (ω)}(t)×{tilde over (ρ)})+{tilde over(α)}(t)×{tilde over (ρ)}  Eq. 1

-   -   where {tilde over (ω)}(t) is the measured or calculated angular        velocity of the head, {tilde over (α)}(t) is the measured or        calculated angular acceleration of the head and {tilde over (ρ)}        is a displacement between the sensor and the location of        interest determined from the head anthropometry.

In accordance with an aspect of the present invention, the position ofeach sensor assembly relative to the location of interest can berepresented as a time-varying function. By a “time-varying function,” itis meant that the spatial relationship defined by the function can varyover the time period of a given impact. For example, in any significantimpact, the brain will move around in the skull, such that the positionof tissue located at the location of interest relative to any externallandmarks on the head varies for a period of time after the impact.Accordingly, the relative location can be expressed as a function of thekinematic values measured at the sensors as well as the measured headanthropometry. In accordance with an aspect of the present invention,the data transform component 14 can incorporate a model of the movementof the brain, given a set of anthropometric parameters describing thehead, when exposed to various kinematics and kinetics, including linearacceleration, angular acceleration, angular velocity, orientationchange, impact force, and energy absorption among others. For example,the location of interest can be represented as a plurality oftime-varying functions, with a given function selected according to thekinematic values measured at the sensor assembly 12. By tracking themovement of the location of interest induced by the impact, the linearacceleration, angular acceleration, angular velocity, and orientationchanges experienced there, and the corresponding physiological effects,can be calculated more accurately via this “deformable body” approach.

In this implementation, translation of the sensor data at the datatransform component 14 can be accomplished according to the followingrelationship between the acceleration at a sensor, ã_(mouth)(t), and theacceleration at the location of interest ã_(INT)(t).

{tilde over (α)}_(INT)(t)={tilde over (α)}_(mouth)(t)+{tilde over(ω)}(t)×({tilde over (ω)}(t)×{tilde over (ρ)}(t))+{tilde over(α)}(t)×{tilde over (ρ)}(t)+{tilde over ({umlaut over(ρ)}_(r)(t)+2{tilde over (ω)}(t)×{tilde over ({dot over (ρ)}_(r)(t)  Eq.2

where {tilde over (ω)}(t) is the measured or calculated angular velocityof the head, {tilde over (α)}(t) is the measured or calculated angularacceleration of the head, {tilde over (ρ)}(t), {tilde over ({dot over(ρ)}_(r)(t) and {tilde over ({umlaut over (ρ)}_(r)(t) are functionsrepresenting time-varying displacement, velocity and acceleration,respectively between the sensor and the location of interest of the headand determined from the head anthropometry and the kinematic datameasured at the sensors.

The calculated kinematics and kinetics at the location of interest,including acceleration, is provided to a system interface 16, where theinformation is provided to an observer in a human-comprehensible form.For example, the kinematic and kinetic values associated with a headimpact and the various measured physiological parameters of a user inreal time can be shown to an observer at an associated display. Themeasured data can, for instance, be used to score a boxing or MMAcompetition or provide supplementary content to enrich the fanexperience in person or remotely.

To enhance the safety of these events or other events likely to producesignificant impacts to the head and neck, the measured and calculatedkinematic and kinetic data can be displayed to an observer and/ortrigger a remote warning device(s) when a user exceeds a critical headimpact or physiological threshold. For example, the system interface 16can include an on-board warning device that alerts the observer when thecalculated acceleration exceeds a threshold value. Where desired, thesensor data can be used to activate an associated intervention system,human or automated, to prevent injury to the user. The system interface16 can also provide quantitative measures for correlation withpost-event neurological assessment, including physical exams, bloodexams, genetic typing, and imaging modalities such as coherencetomography, magnetic resonance imaging, diffusion tensor imaging, andpositron emission tomography, and the like. It is believed that anindividual's susceptibility to many neurocognitive disorders later inlife can be enhanced even by repeated minor impacts to the head.Accordingly, the system interface 16 can be configured to tabulate headimpact cumulatively, such as during training or over the course of anathlete's career or portion thereof, as an aid to diagnosis, study, orprevention of long-term neurocognitive disorders, including Parkinson'sdisease, loss of memory, dementia pugilistica, second impact syndrome,psychiatric disorders, and Alzheimer's disease.

FIG. 2 illustrates a classification system 100 in accordance with anaspect of the present invention. The classification system 100 comprisesat least one sensor assembly 102 deployed as to be substantially fixedlyengaged with a human head. In the illustrated implementation, the sensorassembly 102 is engaged with the head of a participant in an athleticevent, for example, as part of a mouthpiece or on a surface of a helmet,though any suitable substantially fixed engagement may be used. Forexample, the sensor assembly 102 could also or instead be located in areplacement tooth implant, an auditory canal implant, a helmet liner,headband, eyewear, stuck to the skin surface, inserted into the nasalcavity, or even as a direct attachment to the head via a skull anchor orthe like. The sensor assembly 102 is operative to measure the kinematicsof the head along any desired axis, including three mutually orthogonalaxes, at the location of the sensor as well as measuring the angularacceleration, angular velocity, and orientation of the head about anycoincident or non-coincident axis or set of axes.

In accordance with an aspect of the present invention, theclassification system 100 is configured to measure the kinematicsinduced by an impact to the head monitored by the sensor assembly 102and categorize the impact as one of a plurality of event classes viacalculated kinematics and kinetics at any desired location on the head.In one implementation, the various event classes can correspond toranges of likelihood for various head and neck injuries given themeasured impact, such as traumatic brain injuries, concussions,sub-concussion injuries, facial fractures, skull fractures, jawfractures, and spinal cord injuries. It will be appreciated thattraining data for a classifier system can be initially derived fromcadaver studies, animal studies, and/or computer or mechanicalsimulations and refined with data collected during usage of the device.For example, a first set of classes could represent ranges ofprobabilities of a concussion or sub-concussion injury given themeasured impact, a second set of classes could represent ranges ofprobabilities of a skull fracture given the measured impact, and a thirdset of classes could represent ranges of probabilities of neck injurygiven the measured impact. From the determined class, an observer, suchas a coach or trainer, can make decisions about a user's, such as anathlete's, further participation in an event or the desirability ofadditional protective and/or diagnostic measures. Further, thedetermined event class can provide an instantaneous on-site or off-sitetriage tool for a physician in diagnosing and treating a potentialinjury to the head or neck arising from the measured impact.

In another implementation, the various event classes can represent anorigin or associated type of the impact. For example, where the athleticevent is a boxing match, the various event classes could represent ahook, an oblique hook, a jab to the forehead or face, an uppercut, across, or an overhand punch. The classes could be further refined toidentify the type and handedness of a punch or other strike (e.g., leftor right). In an MMA match, the classes could be further expanded toinclude various kicks as well as the impact of elbows and knees to thehead. For American football, the type and severity of contact, forexample, helmet-to-helmet, helmet-to-shoulder, helmet-to-foot,helmet-to-object, helmet-to-elbow, helmet-to-knee, and/orhelmet-to-ground—can be ascertained. And for soccer, head-to-headcontacts can be delineated from head-to-elbow or head-to-goalpostimpacts. For various contact sports, the classes can represent the typeand severity of an impact between two players, such that the pluralityof impact classes can include a bare head-to-bare head contact class, abare head-to-knee class, a bare head-to-elbow class, a bare head-to-footclass, a bare head-to-shoulder class, bare head-to-object class, barehead-to-body class and a bare head-to-ground class.

Such information could be utilized for scoring purposes, possibly bybeing provided to a graphical user interface for a recorded or computergenerated “instant replay” of the action as well as for summarizing theaction of a match for later review. Further, in accordance with anaspect of the present invention, the event class information can be usedto provide a computer simulation of the match, for example, to enhancethe viewing experience for spectators or to drive advanced brain injuryfinite element models.

To this end, the sensor data is provided to a processing component 110configured to provide a human-comprehensible output from the sensordata. A sensor interface 112 is configured to determine kinematics andkinetics, including a linear acceleration, at a location of interest,such as the center of gravity of the head, from the sensor data. It willbe appreciated that the transform of the kinematics data from the sensorto provide kinematic and kinetic data at the location of interest can beperformed either by assuming a static tissue location—“rigid body”—orwith a dynamic tissue location represented by a time-varyingfunction—“deformable body”.

The transformed sensor data is then provided to a feature extractor 114that extracts a plurality of features from the transformed data. Inaccordance with an aspect of the present invention, the plurality offeatures can be selected to include at least one feature that is afunction of the kinematics and/or kinetics of the head at the locationof interest.

One set of parameters that are useful as classification features can bederived as functions of the linear acceleration at the location ofinterest. For example, a magnitude of the acceleration at the locationof interest can be determined from the acceleration along each of thethree coordinate axes, and a resultant jerk at the location of interestcan be calculated as the time derivative of the magnitude of theacceleration. Similarly, a direction of the acceleration can bedetermined from the acceleration along each axis. The change in thevelocity of the head in a given direction, or delta-V, can be determinedby integrating the determined acceleration of the location of interestalong that direction, and a corresponding linear momentum can bedetermined as the product of the change in velocity and a mass of thehead. For example, the mass of the head can be estimated from headanthropometry. Similarly, a kinetic energy of the head can be determinedas one-half the product of the mass of the head and the square of thechange in velocity, and a power imparted to the head can be determinedas the time derivative of the kinetic energy. The metrics can alsoinclude any of a peak pressure, a strain, a strain rate, a product ofthe strain and strain rate, a von Mises stress, a strain energy, and asheer stress. Each of these metrics can be represented as a time series,with multiple values for each metric each representing associated timesduring the impact.

Several additional metrics can be derived from the measured andcalculated linear acceleration values when the location of interest is acenter of gravity of the head, such as an impact force along each axis,calculated as the product of the mass of the head and the accelerationat the center of gravity along each axis, and a magnitude of the impactforce. A loading rate can be determined as the time derivative of theimpact force, and minimum and maximum values of this loading rate can beutilized as features. A duration of the impact can be calculated basedon the length of the loading and unloading pulse after initial contactas based on impact force.

A value representing the Gadd Severity Index (GSI) can be calculated as:

$\begin{matrix}{{G\; S\; I} = {\int_{0}^{T}{{{\overset{\sim}{a}}_{R}(t)}^{2.5}{t}}}} & {{Eq}.\mspace{14mu} 3}\end{matrix}$

where ã_(R)(t) is the resultant magnitude of the calculated linearacceleration at the center of gravity expressed as a multiple of thestandard gravitational acceleration at Earth's surface (g=9.81 m/s²),and the period [0:T] is an essential impact duration. In oneimplementation, this duration is selected to be fifteen milliseconds.

A value for the Head Injury Criterion (HIC) can be calculated as:

$\begin{matrix}{{H\; I\; C} = {\left( {t_{2} - t_{1}} \right)\left\lbrack {\frac{1}{t_{2} - t_{1}}{\int_{t\; 1}^{t\; 2}{{{\overset{\sim}{a}}_{R}(t)}{t}}}} \right\rbrack}^{2.5}} & {{Eq}.\mspace{14mu} 4}\end{matrix}$

where ã_(R)(t) is the resultant magnitude of the calculated linearacceleration at the center of gravity expressed as a multiple of thestandard gravitational acceleration and the period [t₁:t₂] is a timeperiod for which the HIC is maximized, referred to as the HIC duration.In one implementation, the HIC duration, equal to t₂-t₁, can also beutilized as a classification feature.

A Skull Fracture Correlate (SFC) value can be calculated as:

$\begin{matrix}{{S\; F\; C} = {\left\lbrack \frac{\max \left( {{Delta} - {V_{R}(t)}} \right)}{H\; I\; C\mspace{14mu} {Duration}} \right\rbrack \frac{1}{g}}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$

where g is standard gravitational acceleration and Delta-V_(R)(t) is theresultant change in velocity of the head.

A second set of parameters useful for event classification are derivedas functions of the angular velocity and angular acceleration of thehead. For example, a magnitude of the angular acceleration can bedetermined from the acceleration about each of the three coordinateaxes, and a magnitude of the angular velocity can be determined from thevelocity about each of the three coordinate axes. A jerk resulting fromthe angular acceleration can be calculated as the time derivative of themagnitude of the angular acceleration. A head angular momentum can bedetermined from the angular velocity about each axis and a correspondingmoment of inertia. The moments of inertia can be estimated from the headanthropometry. A magnitude of the angular momentum can be determinedfrom the angular momentum about each of the three coordinate axes. Eachof these metrics can also be represented as a time series, with multiplevalues for each metric each representing associated times during theimpact.

A Generalized Acceleration Model for Brain Injury Threshold (GAMBIT) canbe calculated as:

$\begin{matrix}{{{GAMBIT}(t)} = \left\lbrack {\left( \frac{{\overset{\sim}{a}}_{R}(t)}{{\overset{\sim}{a}}_{C}} \right)^{2.5} + \left( \frac{{\overset{\sim}{\alpha}}_{R}(t)}{{\overset{\sim}{\alpha}}_{C}} \right)^{2.5}} \right\rbrack^{\frac{1}{2.5}}} & {{Eq}.\mspace{14mu} 6}\end{matrix}$

where ã_(R)(t) is the resultant magnitude of the linear acceleration atthe center of gravity expressed as a multiple of the standardgravitational acceleration, ã_(C) is a critical linear accelerationequal to two hundred fifty times the standard gravitation acceleration,α_(R)(t) is a resultant magnitude of the angular acceleration, and α_(C)is a critical angular acceleration equal to twenty-five thousand radiansper second.

A Weighted Principal Component Score (wPCS) can be calculated as:

$\begin{matrix}{{wPCS} = {k_{lat} \times 10\left( {\begin{bmatrix}{{k_{GSI}\left( \frac{{GSI} - {GSI}_{m}}{{GSI}_{sd}} \right)} + {k_{HIC}\left( \frac{{HIC} - {HIC}_{m}}{{HIC}_{sd}} \right)} +} \\{{k_{LIN}\left( \frac{{\max \left( {{\overset{\sim}{a}}_{R}(t)} \right)} - a_{m}}{a_{sd}} \right)} + {k_{ROT}\left( \frac{{\max \left( {{\overset{\sim}{\alpha}}_{R}(t)} \right)} - \alpha_{m}}{\alpha_{sd}} \right)}}\end{bmatrix} + 2} \right)}} & {{Eq}.\mspace{14mu} 7}\end{matrix}$

where is k_(lat) is a weight with a value of 1 for a lateral impact,k_(GSi) is a weight with a value of 0.4718, k_(HIC) is a weight with avalue of 0.4720, k_(LIN) is a weight with a value of 0.4336, k_(ROT) isa weight with a value of 0.2164, HIC_(m) is a mean value of the HIC overa plurality of sample impacts, HIC_(sd) is a standard deviation of theHIC over the plurality of sample impacts, GSI_(m) is a mean value of theGSI, GSI_(sd) is a standard deviation of the GSI, a_(m) is a mean valueof the linear acceleration at the center of gravity, a_(sd) is astandard deviation of the linear acceleration at the center of gravity.

A Head Impact Power (HIP) can be calculated as:

HIP=m_(head)└{tilde over (α)}_(CGX)(t)∫{tilde over(α)}_(CGX)(t)dt+{tilde over (α)} _(CGY)(t)∫{tilde over(α)}_(CGY)(t)dt+{tilde over (α)} _(CGZ)(t)∫{tilde over(α)}_(CGZ)(t)dt┘++I _(X){tilde over (α)}_(X)(t)∫{tilde over(α)}_(X)(t)+I _(Y){tilde over (α)}_(Y)(t)∫{tilde over (α)}_(Y)(t)+I_(Z){tilde over (α)}_(Z)(t)∫{tilde over (α)}_(Z)  Eq. 8

where m_(head) is a mass of the head, determined from anthropometric isdata, ã_(CGX) is an acceleration at the center of gravity along aanterior-posterior axis, ã_(CGY) is an acceleration at the center ofgravity along a lateral axis, ã_(CGZ) is an acceleration at the centerof gravity along an cranio-caudal axis, α_(X) is an angular accelerationabout a anterior-posterior axis, αY is an angular acceleration about alateral axis, α_(Z) is an angular acceleration about an cranio-caudalaxis, I_(X) is a head mass moment of inertia about a anterior-posterioraxis, I_(Y) is a head mass moment of inertia about a lateral axis, andI_(Z) is a head mass moment of inertia about an cranio-caudal axis.

A number of additional features can be determined by modeling themeasured impact in a finite element model of the brain. For example,features can be generated corresponding to percentages of the volume ofthe brain experiencing various levels of principal strain. In theillustrated implementation, each of a first percentage of the brainvolume experiencing a principal strain exceeding five percent, a secondpercentage of the brain volume experiencing a principal strain exceedingten percent, and a third percentage of the brain volume experiencing aprincipal strain exceeding fifteen percent can be used as features.Similarly, a dilation damage measure (DDM) can be calculated from themodel as a percentage of brain volume experiencing a negative pressureless than 101.4 kilopascals. A relative motion damage measure (RMDM) canbe calculated as:

$\begin{matrix}{{RMDM} = \frac{ɛ(t)}{ɛ_{F}\left( {t,{\overset{.}{ɛ}(t)}} \right)}} & {{Eq}.\mspace{14mu} 9}\end{matrix}$

where ε(t) is a bridging vein strain, as determined by the finiteelement model, and ε_(F)(t,{dot over (ε)}(t)) is a strain associatedwith bridging vein failure at a given strain rate, as determined by thefinite element model.

Similarly, a possibility of neck injuries can be accessed via acalculated force at the occipital-cervical junction along each axis anda determined occipital moment about each axis, as well as a magnitude ofthe force and the occipital moment. A Neck Injury Criterion (N_(ij)) canbe calculated as:

$\begin{matrix}{N_{ij} = {\max\left\lbrack {\frac{{\overset{\sim}{F}}_{occZ}(t)}{F_{Zcrit}} + \left( \frac{{{{\overset{\sim}{F}}_{occY}(t)}*d} + {\overset{\sim}{M}}_{occX}}{M_{Xcrit}} \right)} \right\rbrack}} & {{Eq}.\mspace{14mu} 10}\end{matrix}$

where {tilde over (F)}_(occZ)(t) is the occipital force along ancranio-caudal axis, {tilde over (F)}_(occY)(t) is the occipital forcealong a lateral axis, {tilde over (M)}_(occX) is the occipital momentabout a anterior-posterior axis, F_(Zcrit) is a critical occipital axialforce equal to 6806 Newtons, M_(Xcrit) is a critical occipital momentequal to one hundred thirty-five Newton-meters, and d is a distanceequal to 0.01778 meters.

The calculated features are then provided to a pattern recognitionclassifier that selects an event class representing the impact from aplurality of event classes. The pattern recognition classifier 116 canutilize any of a number of classification techniques to select anappropriate event class from the plurality of numerical features.Further, the pattern recognition classifier 116 can utilize featuresthat are not derived from the sensor data, such as an age, height, orweight of the user and one or more numerical parameters derived from amedical history of the user, such as a recorded history of previoussensed head impacts. In one implementation, the pattern recognitionclassifier 116 comprises a rule based classifier that determines anevent class according to a set of logical rules. Alternatively, thepattern recognition classifier 116 can comprise a Support Vector Machine(SUM) algorithm or an artificial neural network (ANN) learning algorithmto determine an occupant class for the candidate occupant. A SVMclassifier can utilize a plurality of functions, referred to ashyperplanes, to conceptually divide boundaries in an N-dimensionalfeature space, where each of the N dimensions represents one feature(e.g., layer characteristic) provided to the SVM classifier. Theboundaries define a range of feature values associated with each class.Accordingly, an output class can be determined for a given inputaccording to its position in feature space relative to the boundaries.

An ANN classifier comprises a plurality of nodes having a plurality ofinterconnections. The layer characteristic values are provided to aplurality of input nodes. The input nodes each provide these inputvalues to layers of one or more intermediate nodes. A given intermediatenode receives one or more values from previous nodes. The receivedvalues are weighted according to a series of weights established duringthe training of the classifier. An intermediate node translates itsreceived values into a single output according to a transfer function atthe node. For example, the intermediate node can sum the received valuesand subject the sum to a binary step function. These outputs can in turnbe provided to addition intermediate layers, until an output layer isreached. The output layer comprises a plurality of outputs, representingthe output classes of the system. The output class having the best value(e.g., largest, smallest, or closest to a target value) is selected asthe output class for the system.

The selected event class is provided to a post-processing component 118configured to provide the event class to a human operator in ahuman-comprehensible form. For example, the human operator can includeone or more of an athlete, a coach of the athlete, a family member ofthe athlete, a trainer of the athlete, an official associated with anathletic event, and an audience associated with the athletic event. Thepost-processing component 118 can include a display that simply displaysa label associated with the class, a computer simulation or animation,or even a simple auditory or visual indicator that alerts an observerthan a user may have sustained an impact that falls within the selectedevent class. In one implementation, the post-processing component 118includes a graphical user interface to allow a user, such as an audiencemember, to customize the presentation of the event class or other dataderived from the sensor assembly 102. For example, the graphical userinterface could be configured to allow a user to define a conditionrelated to the representation of the determined acceleration and place awager contingent on a future occurrence of the defined condition.

In view of the foregoing structural and functional features describedabove, a methodology in accordance with various aspects of the presentinvention will be better appreciated with reference to FIGS. 3-8. While,for purposes of simplicity of explanation, the methodologies of FIGS.3-8 are shown and described as executing serially, it is to beunderstood and appreciated that the present invention is not limited bythe illustrated order, as some aspects could, in accordance with thepresent invention, occur in different orders and/or concurrently withother aspects from that shown and described herein. Moreover, not allillustrated features may be required to implement a methodology inaccordance with an aspect the present invention.

FIG. 3 illustrates one example of a method 140 for determining one ofkinematics and kinetics at an arbitrary location of the head inaccordance with an aspect of the present invention. For the purpose ofexample, this method describes determining kinematics and kinetics at acenter of gravity of a user's head, as it has been determined, inaccordance with an aspect of the present invention, kinematic andkinetic data at the center of gravity is a useful predictor inclassifying head impact events. The method begins at 142, where a sensorassembly is initialized for use for a given user and attached to theuser's head in a substantially rigid manner to an ambient-accessiblesurface of the user's head (e.g., in a mouthpiece or an implant withinthe auditory canal). For example, various measurements of the user'shead can be taken and one or more values or time varying functionsrepresenting a center of gravity of the head of that user can bedetermined. In one implementation, a position and orientation of thesensor within the mouthpiece as it is worn by a user can be determinedvia lateral and anteroposterior x-rays to directly register the locationof the sensors relative to a center of gravity.

At 144, linear acceleration data is produced from the sensor assemblyrepresenting acceleration experienced by the user's head at the site ofthe at least one sensing device. In one implementation, the sensorassembly is configured to be responsive only to impacts producing apredetermined acceleration, such that impacts below a thresholdacceleration are not stored or transmitted. The sensor assembly can beconfigured to conserve power in a sleep mode, with the sensors onlypowered fully when collecting data in bursts. It will be appreciatedthat the sensor assembly can include one or more signal conditioningelements configured to take raw voltage data from sensors and convert toan appropriate signal for storage/transmission. For example, the signalconditioning elements can include one or more amplifiers, integrators,filters, and multiplexers for providing a coherent signal for one orboth of transmission and storage. At 146, angular velocity dataindicative of an angular velocity of the user's head is provided by thesensor assembly. At 148, angular acceleration data indicative of anangular acceleration of the user's head is provided by the sensorassembly. At 150, orientation data indicative of an orientation of theuser's head is provided by the sensor assembly.

At 152, the sensor data is transmitted to a processing component, forexample, via a wired or RF wireless connection. At 154, a location ofthe center of gravity of the user's head relative to a position of theat least one sensing device is determined as a function of time from theregistered location data and the sensor data. For example, theprocessing unit can comprise a look-up table containing various timevarying functions representing the position of the center of gravity,and a given function can be selected according to associated ranges oflinear acceleration, angular velocity, angular acceleration, andorientation data measured.

At 156, the acceleration at the center of gravity of the user's head iscalculated as a function of the sensor data, the represented location ofthe center of gravity of the user's head, the angular velocity data, theangular acceleration data, and the orientation data. At 158, thecalculated kinematic and kinetic data are then provided to at least oneof the user and an observer in a human-perceptible form. For example,the mouthpiece could be configured to provide an auditory, visual,and/or tactile stimulus to the user and/or an observer in response to animpact producing a dangerous level of acceleration. Alternatively, thecalculated kinematic and kinetic data can be provided to an observer atan associated display.

FIG. 4 illustrates a methodology 170 for classifying an impact into anevent class in accordance with an aspect of the present invention. At172, at least one of linear acceleration data, angular velocity data,angular acceleration data, and orientation data are acquired from asensor assembly. For example, the sensor assembly can include one ormore of a plurality of linear accelerometers, a plurality of angularvelocity sensors, and a plurality of orientation sensors, and theangular acceleration can be determined from the angular velocity. At174, the sensor data is conditioned to enhance the raw sensor data,eliminate obvious noise, and otherwise prepare the sensor data forfurther processing. At 176, the conditioned sensor data and knownanthropometric parameters of a user are used to calculate the linear androtational kinematics and kinetics at the center of gravity of the head.

At 178, a plurality of features are extracted from the sensor data. Inaccordance with an aspect of the present invention, a subset of at leasttwo of the plurality of features can be derived from the calculatedkinematics and kinetics at the center of gravity of the head. Inaddition, the plurality of features can include an age, height, orweight of the user as well as one or more numerical parameters derivedfrom a medical history of the user. The extracted features represent thecircumstances of the impact as a vector of numerical measurements,referred to as a feature vector. At 180, the feature vector is relatedto a most likely event class, based on an appropriate classificationtechnique. For example, the feature vector can be classified via aseries of logical rules at an appropriate rule-based expert system.Alternatively, the classification can be performed by a statistical orneural network classifier. In one implementation, the classificationtechnique further provides a confidence value representing thelikelihood that the pattern is a member of the selected event class. Theconfidence value provides an external ability to assess the correctnessof the classification. For example, a classifier output may have a valuebetween zero and one, with one representing a maximum certainty.

At 182, the selected event class is conveyed to the user or an observerin a human-comprehensible form. For example, a label associated with theclass can be displayed, a computer simulation can be generated torepresent the selected event, or an auditory or visual indicator canalert an observer, such as a coach or trainer, when an event classrepresenting a likelihood of a specific neck or head injury that isabove a predetermined threshold is selected. Where a confidence value isgenerated, it can also be provided to the observer to aid in decisionsas to the user's further participation in the event or to aid in anymedical diagnosis.

FIG. 5 illustrates a methodology 200 for utilizing an impact monitoringsystem in accordance with an aspect of the present invention to augmentthe experience of a non-participant of an athletic event. At 202, atleast one condition, related to the athletic event, is provided by anon-participating user and received at the impact monitoring system. Forexample, the at least one condition can be provided to an systeminterface of the impact monitoring system, such that the various impactsreceived or delivered by individuals outfitted with the sensor assemblydescribed previously can be compared to the defined conditions.

It will be appreciated that the at least one condition can vary with thedesired application. For example, a condition can relate to an impactreceived by a specific participant, such as an impact having a linearacceleration at the center of gravity of the head greater than athreshold value occurring to a specified participant or an impact thatfalls within a particular event class (e.g., a helmet-to-helmet impactin American football). The condition could be as simple as theoccurrence of any significant impact to a participant's head. It will beappreciated that the condition does not need to be specific to aparticular impact, and could represent, for example, a threshold for acumulative force or acceleration experienced by a given participant.Alternatively, a condition can include the detection of a specific eventclass, such as a particular impact source. For example, in a boxingmatch, the condition can be the occurrence of a particular kind of punchor a punch producing a force or imposed acceleration above a thresholdvalue.

At 204, impacts to participants in the athletic event are monitored. At206, at least one characteristic is determined for any detected impacts.The determined characteristic can include a magnitude of a given impact,an associated location of the impact, or an event class of the impact,such as an impact source, a “legal/illegal” hit determination, or alikelihood of injury represented by the impact. At 208, it is determinedif any of the defined conditions are satisfied by the determinedcharacteristic. If not (N), the methodology 200 returns to 202 tocontinue monitoring for impacts. If so (Y), a user is alerted that thecondition has been satisfied at 210, and the methodology 200 returns to202 to continue monitoring for impacts.

It will be appreciated that the method of FIG. 5 can be used for any ofa number of purposes. In one example, the non-participant can be aparent, coach, official, or other person with a direct interest in thewell-being of a participant, and the condition can be any impact to thehead of the participant or any impact to the participant's head above athreshold level of force or acceleration.

As athletes are becoming faster and regulations are becoming morecomplex, it has become increasingly difficult for referees to correctlyenforce rules of play. This difficult task is made harder by accusationsof bias in enforcing these rules. Currently, the majority of rules areenforced based on the subjective observations of the referees. Toprovide officials with an objective source of data to aid in ruleimplementation, the condition can be defined as the receipt orinitiation of various types of impacts. This information could be usedto provide objective scoring for various sports (e.g., boxing and MMA),or rules verification in others (e.g., detecting illegal contact to thehead in American football).

The method can also be used for directly entertaining a non-participant.For example, the conditions can represent wagers placed by observers,with the determined characteristics representing a cumulative scoringtotal, via number or magnitude of impacts imposed or received within aregion of interest. It will be appreciated that the conditions can bedefined such that only impacts above a threshold magnitude are includedin the scoring. Alternatively, the conditions could represent scoringcategories in a fantasy sports game, for example, for boxing, Americanfootball, or MMA, with the characteristics representing various impactsources (e.g., uppercut, jab, kick, etc.), impact thresholds, andcumulative received or imposed impact totals.

Finally, an ancillary benefit of the collection of impact statistics isthe ability to share the accumulated statistics with observers, bothlocal and remote. An observer's benefit from watching the event can beenhanced by the ability to identify the forces the player is receivingduring the event. This would be even more useful to an observer with aparticular interest in an individual athlete, such as a parent watchingtheir child play high school football or an individual watching afavorite boxer during a match. The impact monitoring system can includethe ability to graphically display force of impact both instantaneouslyand cumulatively. Similarly, a number and type of impacts exceeding athreshold force or acceleration can be displayed to the observers, alongwith any relevant information from any scoring performed by the system.Finally, where the system identifies the impact source and location, acomputer-generated simulation of the impact can be displayed to theobserver.

FIG. 6 illustrates a methodology 250 for utilizing an impact monitoringsystem in accordance with an aspect of the present invention to monitorthe performance and condition of an athlete in an athletic event. Astechnology has advanced, so have training methods for sports. In thepast, it was acceptable to train based solely on non-biometricinformation such as distance or time of a run. It has been shown,however, that by modulating the intensity of a workout, better resultscan be achieved, often in a shorter time. The impact monitoring systemcan be a powerful aid in measuring the intensity and effectiveness ofvarious training programs, and they can be adjusted for optimalperformance improvement based on the data obtained.

One example of this would be strength training for the neck. It has longbeen thought that emphasizing neck strengthening could improve outcomesin athletes that receive repeated blows to the head by increasing theshock absorbing capability of the neck via its musculature. Likewise,football players that are prone to transient brachial plexus injuries,(commonly termed stingers/burners, or more severe injuries such astransient quadraparesis are often advised that intensive off-season neckstrengthening would decrease the incidence of further injuries. Theimpact monitoring system could objectify the results of such training bycapturing the amount of force received before and after training. Sincethe impact monitoring system can collect these measurementscumulatively, the results could be more effectively interpreted that asystem that just measures peak impacts. For example, for trainingpurposes, it would likely be more useful to know that an athlete hasthirty percent less force acquired throughout a game than knowing theresults of any one impact. Similarly, information received by the impactmonitoring system could be used to determine if a competitor is becomingless effective as the competition progresses. For example, in a boxingmatch, if the amount of force the opponent is receiving from aparticular type of punch is declining, it could be interpreted by theobserver that the athlete of interest is tiring or has a potentialinjury. For example, the boxer could be fatigued or have a hand injury.

Improving the athletes' technique can also prevent these injuries. Tothis end, the impact monitoring system data can be used to determine theeffectiveness of the player's retraining in appropriate technique. Manyof the newer techniques in sports are designed to minimize trauma to thehead. Since these should result in decreased cumulative force registeredby the impact monitoring system, it can be used to assess theeffectiveness of their learning. For example, an American footballplayer can be trained to avoid leading with the head during a tackle.Conversely, other techniques to increase the force applied to anopponent are practiced in various sports. The effectiveness of thesetechniques may be assessed by the impact monitoring system data from theopponent. For example, a boxer may work to improve the technique of apunch to increase force, and measure his or her progress by the increasein the force of impacts imposed on opponents. This could be made morepowerful if coupled with real time video that is today available in mostcompetitive events.

At 252, impacts to participants in the athletic event are monitored. At254, the force or acceleration of the measured impact is added to alibrary of historical data. It will be appreciated that the historicaldata can represent data captured over the duration of a given athleticevent or training session, over the course of all or part of a season oryear, or over multiple years, depending on the desired application. Thehistorical data can be represented by a variety of descriptivestatistics, including one or more of the mean, median, variance,standard deviation, interquartile range, slope and y-intercept of theimpact magnitude against time, and any other appropriate statistics. Ingeneral, the historical data will be specific to a given athlete, andonly impacts associated with that athlete will be stored. It will beappreciated, however, that an impact monitoring system can storemultiple libraries of historical data associated with various athletesof interest.

At 256, an expected range for impacts associated with a given athlete isdetermined from the historical data. For example, the expected range canbe set around an average (e.g., mean or median) performance of theathlete over a given time period during or preceding an event orpractice, and the range can be set around the average or another valuederived from the average (e.g., the average plus or minus a desiredoffset value representing improvement) according to an appropriatedeviation measure (e.g., standard deviation or interquartile range).Alternatively, the range can be set according to a trend lineestablished from the historical data, to represent a continuation ofobserved improvement. At 258, it is determined if a given impact oraveraged series of impacts delivered or received by the athlete ofinterest fall within the established range. If the measured impact oraverage is within the defined range (N), the methodology 250 returns to252 to continue monitoring impacts. If the measured impact or averagefalls outside of the defined range (Y), a user, such as a coach ortrainer, is alerted at 260, and the methodology 250 returns to 252 tocontinue monitoring the athlete's status.

In one implementation, multiple impact parameters can be calculated froma determined force or acceleration, and historical data for each impactparameter can be tracked. Each impact can then be placed in one of aplurality of classes, including a first class in which each of thecalculated impact parameters are within their associated desired ranges,a second class in which none of the calculated impact parameters arewithin their associated desired ranges, and a third class in which atleast one of the calculated impact parameters is within its associateddesired range and at least one of the calculated impact parameters isnot within its associated desired range. Such a system could provideadditional information to a trainer or other observer evaluating anathlete's performance.

FIG. 7 illustrates a methodology 300 for utilizing additional sensors,that is, sensors not directly used to measure impacts, placed on asensor assembly in accordance with an aspect of the present invention tomonitor a status of an athlete during an athletic event. At 302, astatus of the athlete is detected at a sensor on the sensor assembly.For example, the sensor assembly can include one or more sensors fordetecting a temperature, sodium concentration, or location associatedwith the user. In one implementation, the sensor assembly includes anactive radio frequency identification (RFID) device that works inconcert with a tracking system at a location associated with theathletic event to provide a continuous updating of each athlete'sposition.

At 304, it is determined if the measured status is outside of a definedrange. For example, it can be determined if the temperature or sodiumconcentration of the athlete is outside of a normal physiological range.Alternatively, it can be determined if the athlete has left the field ofplay or if the athlete has ventured into a restricted region of thefield of play. If the measured status is within the defined range (N),the methodology 300 returns to 302 to continue monitoring the athlete'sstatus. If the status is outside of the defined range (Y), a user isalerted of the deviation of the status from the defined range at 306,and the methodology 300 returns to 302 to continue monitoring theathlete's status.

It will be appreciated that the location tracking function of the sensorassembly could be useful in multiple contexts. It can often be difficultduring team competitions to determine when a particular athlete ofinterest is on or off the field. A sensor assembly operating asdescribed in FIG. 7 could give an observer an alert that their player ofinterest has entered the competition. For example, the parents of a highschool football player could be alerted when their child is activelycompeting. The method 300 can also be used to determine where the playeris at any particular time on the field of play. In one implementation,the status of the athlete could be updated regularly regardless ofwhether it exceeds the defined range, and a graphical interface could beprovided with the position of all of the players represented by iconsand the observer's player of interest having an icon of a differentshape or color.

The methodology of FIG. 7 could also be used for rule enforcement. Onecommon penalty that could be interpreted more readily with themethodology would be penalties relating to the number and position ofplayers on the field. For example, penalties can be incurred for havingtoo many players on the field or players at incorrect positions on thefield in the sport of American football (e.g., players can be offside ortoo far from the line of scrimmage prior to a pass when not an eligiblereceiver). In one implementation, the players can be represented byicons that change color based on whether they are on or off the playingsurface or according to the athlete's position on the field. Thismethodology could also be used by the individual team to preventpenalties.

In other applications, additional sensors placed on the sensor assemblyof the impact monitoring system can be used to detect biometric data andtrigger an event. Elite target shooters minimize body movement toimprove accuracy. The impact monitoring system can be used as a triggerby varying pressure. It can also time the trigger to match biometricssuch as heartbeat or breathing. Further, the sensor assembly can includesome form of tactile indicator to improve the ability of handicappedathletes to participate. A hearing impaired athlete can be alerted tothe start of an event such as a race by the transmission of a signal tothe sensor assembly, with a component on the sensor assembly vibratingto alter the athlete of the starting signal. Thus, the athlete would nothave to rely on vision to replace the sound of a starting signal. Itwould also be possible to transmit this signal to non-impaired athletes.This would allow a faster response to the start of a race versuslistening for a signal and could improve performance.

FIG. 8 illustrates a methodology 350 for enhancing a presentation of anathletic event in accordance with an aspect of the present invention. At352, data, representing an impact applied to a first athlete by a secondathlete, is received at a sensor located at a first location on thefirst athlete. For example, the sensor can be configured to measure atleast one of a linear acceleration, an angular acceleration, an angularvelocity, and an orientation of the head at the associated measurementpoint during the impact to the head, wherein the location of interest isremote from the associated measurement point.

At 354, one of an acceleration and a force at a second location, such asa center of gravity of the first athlete's head, on the first athleteinduced by the impact is determined. For example, a projection of theacceleration or force at the second location along multiple axes can bedetermined, along with a magnitude of the acceleration or force. In oneimplementation, the acceleration or force can be represented as a timeseries, with the acceleration or force and the second locationdetermined at multiple times within the impact. At 356, an impact classassociated with the impact is selected from the determined accelerationor force at the second location. For example, the impact class canrepresent a particular type of strike that can be applied to the firstathlete by the second athlete.

At 358, a representation of the determined one of the acceleration andthe force at the second location on the first athlete is displayed to anaudience of the athletic event. For example, displaying therepresentation of the determined one of the acceleration and the forceat the second location comprises can include displaying a representationof the selected impact class. In one implementation, a graphic or textlabel can be shown to the audience to indicate the selected class. In analternative implementation, the selected class can be displayed as ananimation of the second athlete delivering an impact associated with theselected impact class, the first athlete receiving an impact of theselected impact class, or both. Alternatively, displaying therepresentation of the acceleration or force can include displaying ahistogram of a plurality of impact classes, and updating the histogrameach time one of the plurality of impact classes is selected to showthat the second athlete has delivered another impact of the selectedtype.

In another implementation, the acceleration or force imparted to thefirst athlete can be shown as a cumulative sum, either numerically orgraphically, with a slider or a chart. In one implementation, thecumulative sum can be shown as a reduction of a predetermined startingvalue each time the impact is received. In another implementation, therepresentation of the determined one of an acceleration and a force caninclude a parameter calculated as a function of the determinedacceleration or force. In still another implementation, an indicator canbe displayed to the audience when the determined acceleration or a forceat the second location exceeds a threshold value. A running total ofdiscrete impacts for which the threshold value is exceeded can bemaintained for each athlete over a period of time and displayed to theaudience.

FIG. 9 illustrates a computer system 400 that can be employed toimplement systems and methods described herein, such as based oncomputer executable instructions running on the computer system. Thecomputer system 400 can be implemented on one or more general purposenetworked computer systems, embedded computer systems, routers,switches, server devices, client devices, various intermediatedevices/nodes and/or stand alone computer systems. Additionally, thecomputer system 400 can be implemented as part of the computer-aidedengineering (CAE) tool running computer executable instructions toperform a method as described herein.

The computer system 400 includes a processor 402 and a system memory404. Dual microprocessors and other multi-processor architectures canalso be utilized as the processor 402. The processor 402 and systemmemory 404 can be coupled by any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. The system memory404 includes read only memory (ROM) 408 and random access memory (RAM)410. A basic input/output system (BIOS) can reside in the ROM 408,generally containing the basic routines that help to transferinformation between elements within the computer system 400, such as areset or power-up.

The computer system 400 can include one or more types of long-term datastorage 414, including a hard disk drive, a magnetic disk drive, (e.g.,to read from or write to a removable disk), and an optical disk drive,(e.g., for reading a CD-ROM or DVD disk or to read from or write toother optical media). The long-term data storage can be connected to theprocessor 402 by a drive interface 416. The long-term storage components414 provide nonvolatile storage of data, data structures, andcomputer-executable instructions for the computer system 400. A numberof program modules may also be stored in one or more of the drives aswell as in the RAM 410, including an operating system, one or moreapplication programs, other program modules, and program data.

A user may enter commands and information into the computer system 400through one or more input devices 420, such as a keyboard, atouchscreen, and/or a pointing device (e.g., a mouse). It will beappreciated that the one or more input devices 420 can include one ormore sensor assemblies transmitting acceleration data to the computer400 for further processing. These and other input devices are oftenconnected to the processor 402 through a device interface 422. Forexample, the input devices can be connected to the system bus by one ormore a parallel port, a serial port or a USB. One or more outputdevice(s) 424, such as a visual display device or printer, can also beconnected to the processor 402 via the device interface 422.

The computer system 400 may operate in a networked environment usinglogical connections (e.g., a local area network (LAN) or wide areanetwork (WAN)) to one or more remote computers 430. A given remotecomputer 430 may be a workstation, a computer system, a router, a peerdevice, or other common network node, and typically includes many or allof the elements described relative to the computer system 400. Thecomputer system 400 can communicate with the remote computers 430 via anetwork interface 432, such as a wired or wireless network interfacecard or modem. In a networked environment, application programs andprogram data depicted relative to the computer system 400, or portionsthereof, may be stored in memory associated with the remote computers430.

It will be understood that the above description of the presentinvention is susceptible to various modifications, changes andadaptations, and the same are intended to be comprehended within themeaning and range of equivalents of the appended claims. The presentlydisclosed embodiments are considered in all respects to be illustrative,and not restrictive. The scope of the invention is indicated by theappended claims, rather than the foregoing description, and all changesthat come within the meaning and range of equivalence thereof areintended to be embraced therein.

1. A system for classifying an impact to a head of a human being as oneof a plurality of event classes, the system comprising: a sensorinterface configured to determine an acceleration at a location ofinterest on the human being from provided sensor data in response to theimpact; a feature calculation component configured to calculate aplurality of impact parameters from the determined acceleration at thelocation of interest; a pattern recognition classifier configured toassociate the impact with an associated event class of at least threeassociated event classes according to the calculated plurality of impactparameters; and a post-processing component configured to communicatethe associated event class an observer via an associated output device.2. The system of claim 1, further comprising a sensor assemblypositioned at an associated measurement point on the head and configuredto measure at least one of a linear acceleration, an angularacceleration, an angular velocity, and an orientation of the head at theassociated measurement point during the impact to provide the sensordata, wherein the location of interest is remote from the associatedmeasurement point.
 3. The system of claim 2, wherein the sensor assemblyis located in a mouth guard and a lip guard worn by the human being. 4.The system of claim 1, wherein the pattern recognition classifier isfurther configured to determine a desired range for values of thecalculated impact parameters from historical data for the calculatedimpact parameters for the human being, the at least three event classesincluding a first class in which each of the calculated impactparameters are within their associated desired ranges, a second class inwhich none of the calculated impact parameters are within theirassociated desired ranges, and a third class in which at least one ofthe calculated impact parameters is within its associated desired rangeand at least one of the calculated impact parameters is not within itsassociated desired range.
 5. The system of claim 1, wherein the at leastthree event classes represent ranges of likelihood for an injury to astructure within one of the head and neck given the measured impact. 6.The system of claim 1, wherein the at least three event classesrepresent respective mechanisms by which impacts are delivered, suchthat the associated event class represents a mechanism by which theimpact was delivered.
 7. The system of claim 1, wherein the location ofinterest is a center of gravity of the human being's head.
 8. The methodof claim 1, wherein the location of interest is a location within one ofa frontal lobe, parietal lobe, temporal lobe, occipital lobe,cerebellum, medulla oblongata, pons, thalamus, gyrus, formix, amygdyla,hippocampus, cranium, facial bones, maxilla, mandible, cerebrospinalfluid, occipito-cervical junction, cervical spine, vertebral bodies,spinal cord, spinal nerves, spinal vessels, basal ganglia, pen-vascularspaces, septum, white-gray matter junctions, bridging veins, corpuscallosum, fissure, cavity sinus, meninges, falx, dura, arachnoid, andpia matter of the user.
 9. The system of claim 1, wherein the patternrecognition classifier is one of an artificial neural network and asupport vector machine.
 10. The system of claim 1, wherein thecalculated plurality of impact parameters includes the determinedacceleration at a first time and the determined acceleration at a secondtime which is offset from the first time.
 11. The system of claim 1,wherein the calculated plurality of impact parameters includes anangular acceleration at the location of interest.
 12. The system ofclaim 1, wherein the calculated plurality of impact parameters includesa physical parameter calculated from a finite element model of the humanbeing's head and neck.
 13. The system of claim 12, wherein the physicalparameter is one of a Gadd Severity Index, a von Mises stress, and aHead injury Criterion, an impact force, a strain rate, a loading rate, aGeneralized Acceleration Model for Brain Injury Threshold, a relativemotion damage measure, a dilatational damage measure, a neck injurycriterion, Head Impact Power parameter, a weighted principle componentscore, a strain energy, a cumulative strain damage measure, peakpressure, a principal strain, a sheer stress, and a product of a strainand the strain rate.
 14. The system of claim 1, wherein the calculatedplurality of impact parameters includes an angular velocity at thelocation of interest.
 15. The system of claim 1, wherein the calculatedplurality of impact parameters includes an angular position andorientation at the location of interest.
 16. The system of claim 1,wherein the calculated plurality of impact parameters includes anangular jerk at the location of interest.
 17. The system of claim 1,wherein the calculated plurality of impact parameters includes a linearjerk at the location of interest.
 18. A method for determining a risk ofinjury to a human being comprising: measuring at least one of a linearacceleration, an angular acceleration, an angular velocity and anorientation at a first location on the human being; determining anacceleration at a location of interest on one of a head and a neck ofthe human being from the measured at least one of a linear acceleration,an angular acceleration, an angular velocity and an orientation at thefirst location, the first location being remote from the location ofinterest; calculating a plurality of impact parameters from thedetermined acceleration at the location of interest; associating thecalculated plurality of impact parameters with an associated injuryclass of a plurality of injury classes, each injury class representing arange of probabilities that the human being will suffer an injury to astructure within one of the head and the neck given the calculatedplurality of impact parameters; and communicating the associated eventclass an observer via an associated output device.
 19. The method ofclaim 18, wherein the plurality of injury classes represent ranges ofprobabilities of a concussion.
 20. The method of claim 18, wherein theplurality of injury classes represent ranges of probabilities of atraumatic brain injury (TBI).
 21. The method of claim 18, wherein theplurality of injury classes represent ranges of probabilities of asub-concussion injury.
 22. The method of claim 18, wherein the pluralityof injury classes represent ranges of probabilities of a skull fracture.23. The method of claim 18, wherein the plurality of injury classesrepresent ranges of probabilities of a facial fracture.
 24. The methodof claim 18, wherein the plurality of injury classes represent ranges ofprobabilities of a jaw fracture.
 25. The method of claim 18, wherein theplurality of injury classes represent ranges of probabilities of a neckinjury.
 26. The method of claim 18, wherein the plurality of injuryclasses represent ranges of probabilities of a spinal cord injury. 27.The method of claim 18, wherein the plurality of injury classes includea first set of classes representing respective ranges of probabilitiesof a first injury type and a second set of classes representingrespective ranges of probabilities of a second injury type.
 28. A methodfor classifying an impact in a sporting event, the system comprising:measuring at least one of a linear acceleration, an angularacceleration, an angular velocity and an orientation at a first locationon an athlete participating in the sporting event; determining anacceleration at a location of interest on one of a head and a neck ofthe athlete during the impact from the measured at least one of a linearacceleration, an angular acceleration, an angular velocity and anorientation at the first location, the first location being remote fromthe location of interest; calculating a plurality of impact parametersfrom the determined acceleration at the location of interest;associating the calculated plurality of impact parameters with anassociated impact class of a plurality of impact classes, each impactclass representing an associated type of impact within the sportingevent; and communicating the associated impact class an observer via anassociated output device.
 29. The method of claim 28, wherein theplurality of impact classes represent respective impact sources withinthe sporting event, such that the plurality of impact classes include atleast one of a hook class, an oblique hook class, a jab class, anuppercut class, a cross class, and an overhand punch class.
 30. Themethod of claim 28, wherein the plurality of impact classes represent atype and severity of an impact between two athletes, such that theplurality of impact classes include at least one of a helmet-to-helmetcontact class, a helmet-to-knee class, a helmet-to-shoulder class, ahelmet-to-foot class, a helmet-to-object class, a helmet-to-elbow class,a helmet-to-body class, and a helmet-to-ground class.
 31. The method ofclaim 28, wherein the plurality of impact classes represent a type andseverity of an impact between two players, such that the plurality ofimpact classes include a bare head-to-bare head contact class, a barehead-to-knee class, a bare head-to-elbow class, a bare head-to-footclass, a bare head-to-shoulder class, bare head-to-object class, barehead-to-body class and a bare head-to-ground class.
 32. The method ofclaim 28, wherein the plurality of impact classes represent a handednessof a limb of an second athlete delivering the impact, such that theplurality of impact classes include a left hand class and a right handclass.
 33. The method of claim 28, further comprising determining adesired range for values of the calculated impact parameters fromhistorical data for the athlete, the plurality of impact classescomprising at least a first class representing impacts within thedesired range and a second class representing impacts outside of thedesired range.